Leandro Slipczuk

Infective endocarditis epidemiology over five decades: a systematic review.

Aims To Assess changes in infective endocarditis (IE) epidemiology over the last 5 decades. Methods and Results We searched the published literature using PubMed, MEDLINE, and EMBASE from inception until December 2011. Data From Einstein Medical Center, Philadelphia, PA were also included. Criteria for inclusion in this systematic review included studies with reported IE microbiology, IE definition, description of population studied, and time frame. Two authors independently extracted data and assessed manuscript quality. One hundred sixty studies (27,083 patients) met inclusion criteria. Among hospital-based studies (n=142; 23,606 patients) staphylococcal IE percentage increased over time, with coagulase-negative staphylococcus (CNS) increasing over each of the last 5 decades (p<0.001) and Staphylococcus aureus (SA) in the last decade (21% to 30%; p<0.05). Streptococcus viridans (SV) and culture negative (CN) IE frequency decreased over time (p<0.001), while enterococcal IE increased in the last decade (p<0.01). Patient age and male predominance increased over time as well. In subgroup analysis, SA frequency increased in North America, but not the rest of the world. This was due, in part, to an increase in intravenous drug abuse IE in North America (p<0.001). Among population-based studies (n=18; 3,477 patients) no significant changes were found. Conclusion Important changes occurred in IE epidemiology over the last half-century, especially in the last decade. Staphylococcal and enterococcal IE percentage increased while SV and CN IE decreased. Moreover, mean age at diagnosis increased together with male:female ratio. These changes should be considered at the time of decision-making in treatment of and prophylaxis for IE.

Automatic Valve Plane Localization in Myocardial Perfusion SPECT/CT by Machine Learning: Anatomical and Clinical Validation

Precise definition of the mitral valve plane (VP) during segmentation of the left ventricle for SPECT myocardial perfusion imaging (MPI) quantification often requires manual adjustment, which affects the quantification of perfusion. We developed a machine learning approach using support vector machines (SVM) for automatic VP placement. Methods: A total of 392 consecutive patients undergoing 99mTc-tetrofosmin stress (5 min; mean ± SD, 350 ± 54 MBq) and rest (5 min; 1,024 ± 153 MBq) fast SPECT MPI attenuation corrected (AC) by CT and same-day coronary CT angiography were studied; included in the 392 patients were 48 patients who underwent invasive coronary angiography and had no known coronary artery disease. The left ventricle was segmented with standard clinical software (quantitative perfusion SPECT) by 2 experts, adjusting the VP if needed. Two-class SVM models were computed from the expert placements with 10-fold cross validation to separate the patients used for training and those used for validation. SVM probability estimates were used to compute the best VP position. Automatic VP localizations on AC and non-AC images were compared with expert placement on coronary CT angiography. Stress and rest total perfusion deficits and detection of per-vessel obstructive stenosis by invasive coronary angiography were also compared. Results: Bland–Altman 95% confidence intervals (CIs) for VP localization by SVM and experts for AC stress images (bias, 1; 95% CI, −5 to 7 mm) and AC rest images (bias, 1; 95% CI, −7 to 10 mm) were narrower than interexpert 95% CIs for AC stress images (bias, 0; 95% CI, −8 to 8 mm) and AC rest images (bias, 0; 95% CI, −10 to 10 mm) (P < 0.01). Bland–Altman 95% CIs for VP localization by SVM and experts for non-AC stress images (bias, 1; 95% CI, −4 to 6 mm) and non-AC rest images (bias, 2; 95% CI, −7 to 10 mm) were similar to interexpert 95% CIs for non-AC stress images (bias, 0; 95% CI, −6 to 5 mm) and non-AC rest images (bias, −1; 95% CI, −9 to 7 mm) (P was not significant [NS]). For regional detection of obstructive stenosis, ischemic total perfusion deficit areas under the receiver operating characteristic curve for the 2 experts (AUC, 0.79 [95% CI, 0.7–0.87]; AUC, 0.81 [95% CI, 0.73–0.89]) and the SVM (0.82 [0.74–0.9]) for AC data were the same (P = NS) and were higher than those for the unadjusted VP (0.63 [0.53–0.73]) (P < 0.01). Similarly, for non-AC data, areas under the receiver operating characteristic curve for the experts (AUC, 0.77 [95% CI, 0.69–0.89]; AUC, 0.8 [95% CI, 0.72–0.88]) and the SVM (0.79 [0.71–0.87]) were the same (P = NS) and were higher than those for the unadjusted VP (0.65 [0.56–0.75]) (P < 0.01). Conclusion: Machine learning with SVM allows automatic and accurate VP localization, decreasing user dependence in SPECT MPI quantification.

TCT-820 Multivessel Intracoronary Infusion of Allogeneic Cardiosphere Derived Cells in Dilated Cardiomyopathy: Long Term Outcomes of the Dilated Cardiomyopathy Intervention with Allogeneic Myocardially-Regenerative Cells (DYNAMIC Trial).

nos: 820 824

Molecular Imaging of Vulnerable Coronary Plaque: A Pathophysiologic Perspective

Atherothrombotic events in coronary arteries are most often due to rupture of unstable plaque resulting in myocardial infarction. Radiolabeled molecular imaging tracers directed toward cellular targets that are unique to unstable plaque can serve as a powerful tool for identifying high-risk patients and for assessing the potential of new therapeutic approaches. Two commonly available radiopharmaceuticals—18F-FDG and 18F-NaF—have been used in clinical research for imaging coronary artery plaque, and ongoing clinical studies are testing whether there is an association between 18F-NaF uptake and future atherothrombotic events. Other, less available, tracers that target macrophages, endothelial cells, and apoptotic cells have also been tested in small groups of patients. Adoption of molecular imaging of coronary plaque into clinical practice will depend on overcoming major hurdles, ultimately including evidence that the detection of unstable plaque can change patient management and improve outcomes.

Echo-Doppler determinants of outcomes in patients with unoperated significant mitral regurgitation in current era

Objective One-half of patients with severe symptomatic mitral regurgitation (MR) do not undergo surgery due to comorbidities. We evaluated prognosticators of outcomes in patients with unoperated significant MR. Methods In this observational study, we retrospectively evaluated medical records of 75 consecutive patients with unoperated significant MR. Results All-cause mortality was 39% at 5 years. Non-survivors (n=29) versus survivors (n=46) were: older (77±9.8 vs 68±14, p=0.006), had higher New York Heart Association (NYHA) class (2.7±0.8 vs 2.3±0.8, p=0.037), higher brain natriuretic peptide (1157±717 vs 427±502 pg/mL, p=0.024, n=18), more coronary artery disease (61% vs 35%, p=0.031), more frequent left ventricular ejection fraction <50% (20.7% vs 4.3%, p=0.026), more functional MR (41% vs 22%, p=0.069), higher mitral E/E′ (12.7±4.6 vs 9.8±4, p=0.008), higher pulmonary artery systolic pressure (PASP; 52.6±18.7 vs 36.7±14, p <0.001), more ≥3+ tricuspid regurgitation (28% vs 4%, p=0.005) and more right ventricular dysfunction (26% vs 6%, p=0.035). Significant predictors of 5-year mortality were PASP (p=0.001) and E/E′ (p=0.011) using multivariate regression analysis. Conclusions Patients with unoperated significant MR have high mortality. Elevated PASP and mitral E/E′ were the most significant predictors of 5-year survival in patients with unoperated significant MR. Current American College of Cardiology (ACC)/American Heart Association (AHA) guidelines provide a limited incorporation of echo-Doppler parameters in the preoperative risk stratification of patients with severe MR.

Surgery vs Watchful Waiting for Mitral Regurgitation

Author Affiliations: Department of Cardio-Thoracic Surgery, Erasmus University Medical Center, Rotterdam, the Netherlands (Head, Kappetein); Division of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California (Kaul); Department of Cardiology, Academic Medical Center of the University of Amsterdam, Amsterdam, the Netherlands (Tijssen); Department of Cardiology, Erasmus University Medical Center, Rotterdam, the Netherlands (Serruys).